A Simulation Study of Statistical Approaches to Data Analysis in the Stepped Wedge Design

Ren, Yuqi; Heagerty, Patrick J.

doi:10.1007/s12561-019-09259-x

Cited by 10 publications

(11 citation statements)

References 18 publications

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“…Ren et al. 91 considered permuting the estimated treatment effect and the corresponding z -score obtained from a random intervention model (22), but reported an inflated type I error rate even when the random intervention model is correctly specified. This phenomenon arises likely because the intervention sequence affects both the mean and covariance structures, and the exchangeability assumption fails to hold under the null hypothesis.…”

Section: Estimation and Inference For The Intervention Effectmentioning

confidence: 99%

“…They found that the specification of the random-effects structure (or more generally the heterogeneity term R ik ðj; sÞ 0 a i ) only affected the power of the test, but not the validity, and therefore demonstrated its superiority over the model-based test. Ren et al 91 considered permuting the estimated treatment effect and the corresponding z-score obtained from a random intervention model ( 22), but reported an inflated type I error rate even when the random intervention model is correctly specified. This phenomenon arises likely because the intervention sequence affects both the mean and covariance structures, and the exchangeability assumption fails to hold under the null hypothesis.…”

Section: Estimation and Inference For The Intervention Effectmentioning

confidence: 99%

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Mixed-effects models for the design and analysis of stepped wedge cluster randomized trials: An overview

Hughes

Hemming

et al. 2020

Stat Methods Med Res

121

154

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The stepped wedge cluster randomized design has received increasing attention in pragmatic clinical trials and implementation science research. The key feature of the design is the unidirectional crossover of clusters from the control to intervention conditions on a staggered schedule, which induces confounding of the intervention effect by time. The stepped wedge design first appeared in the Gambia hepatitis study in the 1980s. However, the statistical model used for the design and analysis was not formally introduced until 2007 in an article by Hussey and Hughes. Since then, a variety of mixed-effects model extensions have been proposed for the design and analysis of these trials. In this article, we explore these extensions under a unified perspective. We provide a general model representation and regard various model extensions as alternative ways to characterize the secular trend, intervention effect, as well as sources of heterogeneity. We review the key model ingredients and clarify their implications for the design and analysis. The article serves as an entry point to the evolving statistical literatures on stepped wedge designs.

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Section: Estimation and Inference For The Intervention Effectmentioning

confidence: 99%

Section: Estimation and Inference For The Intervention Effectmentioning

confidence: 99%

Mixed-effects models for the design and analysis of stepped wedge cluster randomized trials: An overview

Hughes

Hemming

et al. 2020

Stat Methods Med Res

121

154

View full text Add to dashboard Cite

show abstract

“…Similar conclusions have been reached in other studies. 3,22,22,27,28 Alternatively, models which allow secular trends to systematically differ between the intervention and control clusters through incorporation of fixed and random group-by-time effects offer a major improvement in terms of bias reduction. Our simulation studies confirmed that models incorporating unstructured cluster-level covariances for the random intervention-by-cluster-by-time interaction terms yielded nominal confidence interval coverage rates and preserved Type 1 error (i.e., models 6 and 9).…”

Section: Discussionmentioning

confidence: 99%

Accounting for Confounding by Time, Early Intervention Adoption, and Time-Varying Effect Modification in the Design and Analysis of Stepped-Wedge Designs: Application to a Proposed Study Design to Reduce Opioid-Related Mortality

Rennert

Heo

Litwin

et al. 2020

Preprint

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Background: Stepped-wedge designs (SWDs) are currently being used in the investigation of interventions to reduce opioid-related deaths in communities located in several states. However, these interventions are competing with external factors such as newly initiated public policies limiting opioid prescriptions, media awareness campaigns, and COVID-19 social distancing mandates. Furthermore, control communities may prematurely adopt components of the intervention as they become available. The presence of time-varying external factors that impact study outcomes is a well-known limitation of SWDs; common approaches to adjusting for them make use of a mixed effects modeling framework. However, these models have several shortcomings when external factors differentially impact intervention and control clusters. Methods: We discuss limitations of commonly used mixed effects models in the context of proposed SWDs to investigate interventions intended to reduce opioid-related mortality, and propose extensions of these models to address these limitations. We conduct an extensive simulation study of anticipated data from SWD trials targeting the current opioid epidemic in order to examine the performance of these models in the presence of external factors. We consider confounding by time, premature adoption of components of the intervention, and time-varying effect modification— in which external factors differentially impact intervention and control clusters. Results: In the presence of confounding by time, commonly used mixed effects models yield unbiased intervention effect estimates, but can have inflated Type 1 error and result in under coverage of confidence intervals. These models yield biased intervention effect estimates when premature intervention adoption or effect modification are present. In such scenarios, models incorporating fixed intervention-by-time interactions with an unstructured covariance for intervention-by-cluster-by-time random effects result in unbiased intervention effect estimates, reach nominal confidence interval coverage, and preserve Type 1 error. Conclusions: Mixed effects models can adjust for different combinations of external factors through correct specification of fixed and random time effects; misspecification can result in bias of the intervention effect estimate, under coverage of confidence intervals, and Type 1 error inflation. Since model choice has considerable impact on validity of results and study power, careful consideration must be given to choosing appropriate models that account for potential external factors.

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“…Next, we fit a marginal model via GEE but used two different variance estimators: the first was the standard GEE sandwich variance estimator; 5 the second employed the

δ_{5}

adjustment proposed by Fay and Graubard, 43 which has been shown to perform well in SWTs with a small number of clusters 14,17 . For both GEEs, we specified an exchangeable working correlation structure, which—although misspecified given the random cluster‐period effects in model ()—is straightforward to implement in most statistical software packages, is often chosen in the analysis of SWTs, and has been shown to perform adequately in SWTs even when the true underlying correlation structure is more complex, especially when the WPC and IPC values are relatively small 12,44 . For all GLMMs and GEEs, we used Wald tests and corresponding 95% CIs.…”

Section: Simulationsmentioning

confidence: 99%

Randomization‐based inference for a marginal treatment effect in stepped wedge cluster randomized trials

Rabideau

Wang

2021

Statistics in Medicine

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In a cross‐sectional stepped wedge cluster randomized trial (SWT), clusters are randomized to crossover from control to intervention at different time periods and outcomes are assessed for a different set of individuals in each cluster‐period. Randomization‐based inference is an attractive analysis strategy for SWTs because it does not require full parametric specification of the outcome distribution or correlation structure and its validity does not rely on having a large number of clusters. Existing randomization‐based approaches for SWTs, however, either focus on hypothesis testing and omit technical details on confidence interval (CI) calculation with noncontinuous outcomes, or employ weighted cluster‐period summary statistics for p‐value and CI calculation, which can result in suboptimal efficiency if weights do not incorporate information on varying cluster‐period sizes. In this article, we propose a framework for calculating randomization‐based p‐values and CIs for a marginal treatment effect in SWTs by using test statistics derived from individual‐level generalized linear models. We also investigate how study design features, such as stratified randomization, subsequently impact various SWT analysis methods including the proposed approach. Data from the XpertMTB/RIF tuberculosis trial are reanalyzed to illustrate our method and compare it to alternatives.

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A Simulation Study of Statistical Approaches to Data Analysis in the Stepped Wedge Design

Cited by 10 publications

References 18 publications

Mixed-effects models for the design and analysis of stepped wedge cluster randomized trials: An overview

Mixed-effects models for the design and analysis of stepped wedge cluster randomized trials: An overview

Accounting for Confounding by Time, Early Intervention Adoption, and Time-Varying Effect Modification in the Design and Analysis of Stepped-Wedge Designs: Application to a Proposed Study Design to Reduce Opioid-Related Mortality

Randomization‐based inference for a marginal treatment effect in stepped wedge cluster randomized trials

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